The Power of Artificial Intelligence in Healthcare

How AI Advances the Healthcare Industry

Artificial Intelligence (AI) in healthcare is evolving quickly. Deep learning, a subfield of machine learning that mimics the way humans gain knowledge, allows machines to identify patterns, utilize algorithms and analyze data to give automated insights. This is revolutionary for the healthcare industry. Machine learning has unlimited potential to provide data-driven clinical decision support to doctors and hospital staff.  

This article will discuss the current roles of AI in healthcare and their benefits. AI simplifies the work of patients, doctors, and healthcare administrators by performing tasks usually done by humans, in less time and at a fraction of the cost, ultimately improving lives and increasing revenue potential for businesses. 

Drug Discovery and Development

There is a lack of advanced technology when it comes to drug development processes, making them time-consuming for the healthcare industry. However, AI can address these issues by helping with rapid drug discovery and aiding in pharmaceutical developments, resulting in high rates of success. This technology can handle massive amounts of data with enhanced automation. AI is able to aid in drug design, chemical synthesis, drug repurposing, and drug screening.  

AI has made a big step forward for many healthcare organizations by enabling them to predict which treatment approaches would be most effective for patients based on their characteristics and the treatment framework. AI algorithms are able to identify potential drug candidates by analyzing data from previous studies. It can also help discovery by predicting drug-protein interactions and their 3D structures. These capabilities help companies develop new drugs more efficiently.  

Algorithms can draw insights from biological datasets that are very complicated for human understanding. This allows pharmaceutical companies to find new uses for existing drugs, which is extremely beneficial, as they are then able to repurpose and reposition these drugs instead of starting from scratch.  

Several pharmaceutical companies such as Pfizer, Roche and Bayer have worked together with IT companies in order to design a platform for the drug discovery process in areas like cardiovascular and immuno-oncology. 

Medical Diagnosis and Treatment

For the past 50 years, disease diagnosis and treatment have been at the center of AI in healthcare. Early rule-based systems had the ability to effectively diagnose and treat disease, but clinical practice did not fully embrace them.  

To this day, AI is used for clinical decision support. Many electronic health record systems, otherwise known as EHRs, have rules within their software offerings. Machine learning is slowly replacing these traditional rule-based systems, allowing for data interpretation using exclusive medical algorithms.  

Natural language processing (NLP) is a subset of artificial intelligence that allows computers to understand and interpret human speech. Instead of wasting valuable time manually interpreting EHRs, NLP uses speech-to-text dictation and formulated data entry to obtain important data from EHR. This allows for doctors to ensure clinical documentation is up to date and accurate while providing patients with care. With the help of NLP, healthcare companies can review large amounts of unstructured clinical data automatically, to help determine clinical trial matching and eligibility. NLP is also used to inform treatment plans.  

AI tools have the ability to support human providers by enabling quicker services, diagnosing patients, and analyzing data to identify genetic history or medical trends that may predispose someone to a particular disease. In healthcare, saving time can directly result in saving lives. For example, any patient that heads to the emergency room with a head injury is given a CT scan. However, the scan may not be assessed for hours because the radiologist has many patients to attend to. If there is a brain bleed, those hours can mean life or death. With the help of AI, the algorithm can listen for the CT scan to process and detect brain bleed immediately. A quicker diagnosis and a known level of urgency can save the patient’s life. Integrating AI and machine learning can be transformative not only for healthcare companies but for every individual patient.  

Learn more about biotechnology firms using AI in efforts to fight cancer – Private-Public Venture Fighting Cancer 

Data Collection and Administrative Tasks

Doctors spend more than two-thirds of their time on paperwork. For every hour of time spent with patients, an additional two hours will be spent on documentation. An extensive AI can help overcome the time-consuming burden of data collection, and help doctors and staff free up their time to prioritize other tasks. Automating administrative tasks will result in huge time and cost savings. 

AI in healthcare administration can provide substantial efficiencies. Applications can include prior authorization, clinical documentation, revenue management, medical record management, optimization of scheduling, and billing and claim processing.  

A recent survey of healthcare executives found that the automation of prior authorizations is the AI application with the most potential. This is a top priority according to healthcare administrations, of whom 88% report this as an administrative burden increasing over the years. AI can identify a patient’s health plan and medical benefits as well as which services need authorization according to their plan benefits. AI can also help with the collection of documentation such as patient history, information, and medical approvals. 

Billing and claims processing is also very costly and time-consuming for administrators. According to the Center for American Progress, billing and insurance-related costs average 13% of physician-care spending and 8.5% of hospital-care spending. These costs burden the healthcare system. Using AI to submit and adjust claims simultaneously streamlines the process and makes it more reliable, all while reducing turnaround time. Additionally, NLP can be used to process doctors’ notes and comments into codes. By automating these manual activities, AI has the potential to dramatically increase the productivity of healthcare providers. 


The Future of Healthcare and AI

Today, AI provides a variety of benefits in the healthcare industry. Whether it’s drug discovery, medical diagnosis, treatment, or data collection, AI has unlimited potential in healthcare. Technology companies are working together with the healthcare industry to solve the toughest data challenges and improve lives. 

The lack of clean, accurate, quality data presents a serious challenge for AI platforms, limiting their ability to provide the “right data” to answer specific questions. This challenge is compounded in the healthcare industry as the annotated data needs to be highly accurate. Even the slightest errors could pose serious health and safety risks.  

From physicians and nurses to physiotherapists and pharmacists, accredited medical experts have deep knowledge and expertise in interpreting and annotating data across a variety of medical literature and documents. 

Learn more about annotation solutions HERE  

Accelerate AI with Annotated Data

Check Out this Article on Why Your Model Performance Problems Are Likely in the Data
ML Model Gains Come From High-Quality Training Data_Innodata

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